“These instances were designed to chew through tough, large-scale machine learning, deep learning, computational fluid dynamics (CFD), seismic analysis, molecular modeling, genomics, and computational finance workloads,” AWS chief evangelist Jeff Barr wrote in a blog post. Deep learning, a trendy type of artificial intelligence, often involves using GPU-backed servers to train neural nets on lots of data so they can make inferences about new data, and now AWS is providing more powerful infrastructure for that computing.

The infrastructure helps bring AWS closer to competitors like Microsoft Azure and IBM SoftLayer when it comes to offering powerful GPU resources in the cloud, so people don’t need to worry about maintaining them on premises. (Microsoft’s Azure N-Series GPU instances are currently in preview.)

There are three sizes for the P2: p2.large (1 GPU, 4 vCPUs, 61 GiB of RAM), p2.8xlarge (8 GPUs, 32 vCPUs, 488 GiB of RAM), and 488 GiB (16 GPUs, 64 vCPUs, and 732 GiB of RAM). The instances are available now in AWS’ US East (Northern Virginia), US West (Oregon), and Europe (Ireland) data center regions. (Azure’s N-Series instances are with available with no more than four GPUs.)

Also today, AWS announced the introduction of a deep learning Amazon machine image (AMI) that can be installed onto VM instances on AWS. The AMI comes with the Caffe, MXNet, TensorFlow, Theano, and Torch open-source deep learning frameworks installed, so customers don’t need to worry about getting them running.